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Record W61028207 · doi:10.1093/pch/19.6.305

Factors affecting adherence to a gluten-free diet in children with celiac disease

2014· article· en· W61028207 on OpenAlexaff
Katherine MacCulloch, Mohsin Rashid

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineGluten freeDiseaseGlutenPediatricsFamily medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: The treatment of celiac disease is a strict, life-long gluten-free (GF) diet. This diet is complex and can be challenging. Factors affecting adherence to the GF diet are important to identify for improving adherence. OBJECTIVE: To identify factors that inhibit or improve adherence to a GF diet in children with celiac disease. METHODS: Patients (<18 years of age) with biopsy-confirmed celiac disease followed by the gastroenterology service at a tertiary care paediatric institution were surveyed using a mailed questionnaire. Factors influencing adherence to a GF diet were scored from 1 to 10 based on how often they were problematic (1 = never, 10 = always). Parents of patients <13 years of age were instructed to complete the survey with their child. Adolescents ≥13 years of age were asked to complete the survey themselves. RESULTS: Of 253 subjects, 126 completed the survey; the median age was 12 years (range two to 18 years). Forty percent were adolescents. Overall, participants reported good adherence at home and school, but lower adherence at social events. Adolescents reported lower adherence compared with parents. Availability of GF foods and cost were the most significant barriers. Other factors identified to help with a GF diet included education for schools/restaurants and improved government support. CONCLUSIONS: Availability, cost and product labelling are major barriers to adherence to a GF diet. Better awareness, improved labelling and income support are needed to help patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.299
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations100
Published2014
Admission routes1
Has abstractyes

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